System and method for network automation in slice-based network using reinforcement learning

ABSTRACT

A method for facilitating allocation of network resources to at least one slice in a communication network includes obtaining information identifying a mapping between network resource allocations and a respective quality of experience (QoE) associated with a user and obtaining information relating to a quality of service (QoS) tolerance for a particular QoE. The method includes providing to a controller for allocating at least a portion of the network resources to the at least one slice: i) the information identifying a mapping between network resource allocations and a QoE; ii) the information relating to the QoS tolerance; and iii) information identifying a current QoE and a target QoE associated with at least one user.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a U.S. National Phase Entry, under 35 U.S.C. § 371,of International Application No. PCT/EP2019/073908, filed on Sep. 6,2019, which claims priority to European Patent Application No. EP18193309.4, filed on Sep. 7, 2018. The International Application waspublished in English on Mar. 12, 2020 as WO 2020/0049181 under PCTArticle 21(2), which is hereby incorporated by reference.

FIELD

In an embodiment, the present invention relates to a communicationsystem. In an embodiment, the invention has particular but not exclusiverelevance to wireless communication systems and devices thereofoperating according to the 3rd Generation Partnership Project (3GPP)standards or equivalents or derivatives thereof. In an embodiment, theinvention has particular although not exclusive relevance to networkautomation in the so-called ‘5G’ (or ‘Next Generation’) systems.

BACKGROUND

3GPP Working Groups are currently defining the 5G system and the 3GPPTSG SA WG2 (SA2) is specifying the system architecture and proceduresfor 5G system. Within SA2, a study item Study of enablers for NetworkAutomation for 5G (eNA) has been created to study requirements and keyissues of network automation for 5G, and the results of this study hasbeen documented in a technical report (TR) 23.791 V0.5.0.

Recently, new study has been proposed is SA2 to investigate how to useuser QoE data to support QoS profile provisioning in TR 23.791 V0.5.0.

SUMMARY

In an embodiment, the present invention provides a method forfacilitating allocation of network resources to at least one slice in acommunication network. The method includes obtaining informationidentifying a mapping between network resource allocations and arespective quality of experience (QoE) associated with a user andobtaining information relating to a quality of service (QoS) tolerancefor a particular QoE. The method includes providing to a controller forallocating at least a portion of the network resources to the at leastone slice: i) the information identifying a mapping between networkresource allocations and a QoE; ii) the information relating to the QoStolerance; and iii) information identifying a current QoE and a targetQoE associated with at least one user.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be described in even greaterdetail below based on the exemplary figures. The present invention isnot limited to the exemplary embodiments. All features described and/orillustrated herein can be used alone or combined in differentcombinations in embodiments of the present invention. The features andadvantages of various embodiments of the present invention will becomeapparent by reading the following detailed description with reference tothe attached drawings which illustrate the following:

FIG. 1 illustrates schematically a generic mobile (cellular or wireless)telecommunication system to which embodiments of the invention may beapplied;

FIG. 2 is a schematic block diagram of a mobile device (user equipment)forming part of the system shown in FIG. 1;

FIG. 3 is a schematic block diagram of a base station apparatus formingpart of the system shown in FIG. 1;

FIG. 4 is a schematic block diagram of a core network node (or OAMnode/application function) forming part of the system shown in FIG. 1;

FIG. 5 illustrates schematically an exemplary machine-learning basedarchitecture in accordance with embodiments of the present invention;

FIGS. 6 to 8 are timing (signalling) diagram illustrating some exemplaryways in which network resource allocation may be carried out using themachine-learning based architecture of FIG. 5;

FIG. 9 illustrates schematically an exemplary QoS architecture inaccordance with embodiments of the present invention;

FIG. 10 illustrates schematically an exemplary QoS bitmap in accordancewith the quality of service architecture shown in FIG. 9; and

FIG. 11 illustrates an exemplary procedure for QoS negotiation for a newapplication using the QoS architecture and QoS bitmap shown in FIGS. 9and 10.

DETAILED DESCRIPTION

Currently, OAM system manages network resources based on network dataand KPIs, which measure and estimate network quality at the networklevel without monitoring the service quality at the user level orservice application level. Examples of network data/KPIs are throughput(e.g., Mbit/s), delay (e.g., msec/packet) or Ethernet/IP packet or TCPsegment loss rate. However, the user QoE is not necessarily consistentwith the network KPI performance. It is a common problem for operatorsthat users complain about poor quality of services while the key networkKPIs are estimated to be within satisfactory limits. This usually leadsto network over-provisioning, operational overhead and wastage ofnetwork resources. This problem becomes more critical with the supportof multiple network slices as each network slice may have different QoErequirements. This clearly demonstrates that there is a need toefficiently allocate network resources while targeting user QoEsatisfaction, especially in slice-based networks.

Moreover, in future networks where user and network data is growingexponentially, it becomes an increasingly challenging and complex toadjust/optimise the network manually in order to deliver highly optimumservice quality for users. There is therefore a need to apply machinelearning and automation in the network to reduce the cost of networkoperation.

QoS in 5G has diverse characteristics brought up by the introduction ofnew applications and services. This may result in new mechanisms toaccommodate new QoS requirements. For example, some non-standardised QoSparameters can be defined and negotiated between OTT service providersand network operators, which are tailored to the nature of newapplications/services and are implemented based on network operators'policy. Current QoS provisioning cannot be adaptive enough toaccommodate a substantial range of new services that have different QoSrequirements and QoS profiles.

In an embodiment, the present invention provides methods and associatedapparatuses that address or at least alleviate (at least some of) theabove issues.

In one aspect, the present invention provides a method for facilitatingallocation of network resources to at least one slice in a communicationnetwork, the method comprising: obtaining information identifying amapping between network resource allocations and a respective quality ofexperience (QoE) associated with a user; obtaining information relatingto a quality of service (QoS) tolerance for a particular QoE; providingto a controller for allocating at least a portion of said networkresources to said at least one slice: i) said information identifying amapping between network resource allocations and a QoE; ii) saidinformation relating to said QoS tolerance; and iii) informationidentifying a current QoE and a target QoE associated with at least oneuser.

In an embodiment, the present invention provides a method for allocatingnetwork resources to at least one slice in a communication network, themethod comprising: obtaining: i) information identifying a mappingbetween network resource allocations and a quality of experience (QoE)associated with a user; ii) said information relating to a quality ofservice (QoS) tolerance for a particular QoE; and iii) informationidentifying a current QoE and a target QoE associated with at least oneuser; and allocating at least a portion of said network resources tosaid at least one slice based on said QoS tolerance and said current QoEand said target QoE.

In an embodiment, the present invention provides a method forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the method comprising: determining at least one QoS parameterassociated with a service to be provided; signaling said determined atleast one QoS parameter to another node using a bitmap comprising atleast a first field and a second field, wherein: the first fieldcomprises a plurality of bits, each bit indicating a respective QoSparameter selected from: a resource type QoS parameter, a priority levelQoS parameter, a packet delay budget QoS parameter, a packet error rateQoS parameter, an averaging window QoS parameter, and a maximum databurst volume QoS parameter; and the second field is configurable toindicate a QoS parameter different to said QoS parameters indicated viathe first field.

In an embodiment, the present invention provides a method forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the method comprising: receiving signaling indicating at leastone QoS parameter using a bitmap comprising at least a first field and asecond field, wherein: the first field comprises a plurality of bits,each bit indicating a respective QoS parameter selected from: a resourcetype QoS parameter, a priority level QoS parameter, a packet delaybudget QoS parameter, a packet error rate QoS parameter, an averagingwindow QoS parameter, and a maximum data burst volume QoS parameter; andthe second field is configurable to indicate a QoS parameter differentto said QoS parameters indicated via the first field; and determining atleast one QoS parameter associated with a service to be provided basedon said bitmap.

In an embodiment, the present invention provides a network function forfacilitating allocation of network resources to at least one slice in acommunication network, the network function comprising: means forobtaining information identifying a mapping between network resourceallocations and a respective quality of experience (QoE) associated witha user; means for obtaining information relating to a quality of service(QoS) tolerance for a particular QoE; means for providing to acontroller for allocating at least a portion of said network resourcesto said at least one slice: i) said information identifying a mappingbetween network resource allocations and a QoE; ii) said informationrelating to said QoS tolerance; and iii) information identifying acurrent QoE and a target QoE associated with at least one user.

In an embodiment, the present invention provides a network function forallocating network resources to at least one slice in a communicationnetwork, the network function comprising: means for obtaining: i)information identifying a mapping between network resource allocationsand a quality of experience (QoE) associated with a user; ii) saidinformation relating to a quality of service (QoS) tolerance for aparticular QoE; and iii) information identifying a current QoE and atarget QoE associated with at least one user; and means for allocatingat least a portion of said network resources to said at least one slicebased on said QoS tolerance and said current QoE and said target QoE.

In an embodiment, the present invention provides a network function forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the network function comprising: means for determining at leastone QoS parameter associated with a service to be provided; means forsignaling said determined at least one QoS parameter to another nodeusing a bitmap comprising at least a first field and a second field,wherein: the first field comprises a plurality of bits, each bitindicating a respective QoS parameter selected from: a resource type QoSparameter, a priority level QoS parameter, a packet delay budget QoSparameter, a packet error rate QoS parameter, an averaging window QoSparameter, and a maximum data burst volume QoS parameter; and the secondfield is configurable to indicate a QoS parameter different to said QoSparameters indicated via the first field.

In an embodiment, the present invention provides a network function forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the network function comprising: means for receiving signalingindicating at least one QoS parameter using a bitmap comprising at leasta first field and a second field, wherein: the first field comprises aplurality of bits, each bit indicating a respective QoS parameterselected from: a resource type QoS parameter, a priority level QoSparameter, a packet delay budget QoS parameter, a packet error rate QoSparameter, an averaging window QoS parameter, and a maximum data burstvolume QoS parameter; and the second field is configurable to indicate aQoS parameter different to said QoS parameters indicated via the firstfield; and means for determining at least one QoS parameter associatedwith a service to be provided based on said bitmap.

In an embodiment, the present invention provides a network function forfacilitating allocation of network resources to at least one slice in acommunication network, the network function comprising a controller, anda transceiver, wherein the controller is configured to: obtaininformation identifying a mapping between network resource allocationsand a respective quality of experience (QoE) associated with a user;obtain information relating to a quality of service (QoS) tolerance fora particular QoE; provide to a controller for allocating at least aportion of said network resources to said at least one slice: i) saidinformation identifying a mapping between network resource allocationsand a QoE; ii) said information relating to said QoS tolerance; and iii)information identifying a current QoE and a target QoE associated withat least one user.

In an embodiment, the present invention provides a network function forallocating network resources to at least one slice in a communicationnetwork, the network function comprising a controller, and atransceiver, wherein the controller is configured to: obtain: i)information identifying a mapping between network resource allocationsand a quality of experience (QoE) associated with a user; ii) saidinformation relating to a quality of service (QoS) tolerance for aparticular QoE; and iii) information identifying a current QoE and atarget QoE associated with at least one user; and allocate at least aportion of said network resources to said at least one slice based onsaid QoS tolerance and said current QoE and said target QoE.

In an embodiment, the present invention provides a network function forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the network function comprising a controller, and atransceiver, wherein the controller is configured to: determine at leastone QoS parameter associated with a service to be provided; signal saiddetermined at least one QoS parameter to another node using a bitmapcomprising at least a first field and a second field, wherein: the firstfield comprises a plurality of bits, each bit indicating a respectiveQoS parameter selected from: a resource type QoS parameter, a prioritylevel QoS parameter, a packet delay budget QoS parameter, a packet errorrate QoS parameter, an averaging window QoS parameter, and a maximumdata burst volume QoS parameter; and the second field is configurable toindicate a QoS parameter different to said QoS parameters indicated viathe first field.

In an embodiment, the present invention provides a network function forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the network function comprising a controller, and atransceiver, wherein the controller is configured to: receive signalingindicating at least one QoS parameter using a bitmap comprising at leasta first field and a second field, wherein: the first field comprises aplurality of bits, each bit indicating a respective QoS parameterselected from: a resource type QoS parameter, a priority level QoSparameter, a packet delay budget QoS parameter, a packet error rate QoSparameter, an averaging window QoS parameter, and a maximum data burstvolume QoS parameter; and the second field is configurable to indicate aQoS parameter different to said QoS parameters indicated via the firstfield; and determine at least one QoS parameter associated with aservice to be provided based on said bitmap.

Aspects of the invention extend to corresponding systems and computerprogram products such as computer readable storage media havinginstructions stored thereon which are operable to program a programmableprocessor to carry out a method as described in the aspects andpossibilities set out above or recited in the claims and/or to program asuitably adapted computer to provide the apparatus recited in any of theclaims.

Each feature disclosed in this specification (which term includes theclaims) and/or shown in the drawings may be incorporated in theinvention independently of (or in combination with) any other disclosedand/or illustrated features. In particular but without limitation thefeatures of any of the claims dependent from a particular independentclaim may be introduced into that independent claim in any combinationor individually.

Overview

FIG. 1 schematically illustrates a mobile (cellular or wireless)telecommunication system 1 to which the embodiments of the presentinvention are applicable.

In this network, users of mobile devices 3 (UEs) can communicate witheach other and other users via respective base stations 5 and a corenetwork 7 using an appropriate 3GPP radio access technology (RAT), forexample, an E-UTRA and/or 5G RAT. It will be appreciated that a numberof base stations 5 form a (radio) access network or (R)AN. As thoseskilled in the art will appreciate, whilst one mobile device 3 and onebase station 5 are shown in FIG. 1 for illustration purposes, thesystem, when implemented, will typically include other base stations andmobile devices (UEs).

Each base station 5 controls one or more associated cells (eitherdirectly or via other nodes such as home base stations, relays, remoteradio heads, distributed units, and/or the like). A base station 5 thatsupports E-UTRA/4G protocols may be referred to as an ‘eNB’ and a basestation 5 that supports NextGeneration/5G protocols may be referred toas a ‘gNBs’. It will be appreciated that some base stations 5 may beconfigured to support both 4G and 5G, and/or any other 3GPP or non-3GPPcommunication protocols.

The mobile device 3 and its serving base station 5 are connected via anappropriate air interface (for example the so-called ‘Uu’ interfaceand/or the like). Neighbouring base stations 5 are connected to eachother via an appropriate base station to base station interface (such asthe so-called ‘X2’ interface, ‘Xn’ interface and/or the like). The basestation 5 is also connected to the core network nodes via an appropriateinterface (such as the so-called ‘S1’, ‘N1’, ‘N2’, ‘N3’ interface,and/or the like).

The core network 7 typically includes logical nodes (or ‘functions’) forsupporting communication in the telecommunication system 1. Typically,for example, the core network 7 of a ‘Next Generation’/5G system willinclude, amongst other functions, control plane functions (CPFs) 10 anduser plane functions (UPFs) 11. In this example, the core network 7 iscoupled to at least one AF 12 (e.g. via the Internet) and an OAM 13system. It will be appreciated that the core network 7 may also includeone or more of the functions shown in FIGS. 5 to 11, such as: at leastone PCF; the SMF; the NWDAF; and the NEF. The OAM system 13 may, forexample, include one or more of: the Central Controller and at least oneAgent of Slice; the OAM MF(s); and the MDAS. The Central Controller andAgent(s) of Slice can be integrated into an OAM MF. From the corenetwork 7, connection to an external IP network 20 (such as theInternet) is also provided.

In order to support various types of users (UEs) and services (serviceproviders), the system 1 communicates data for each UE 3 using one ormore associated network slice. The components of this system 1 areconfigured to allocate network resources across network slices such thata target UE's QoE is satisfied, in an automated machine-learning basedmanner. In order to do so, the system 1 employs a machine learning (ML)based architecture with a plurality of agents (e.g. one agent per slice)which are responsible for allocating network resources based on a targetQoE.

The ML-based architecture includes a central controller that uses QoEtargets rather than QoS targets and learns the best QoS parameters tomeet such QoE targets. When there is a resource deficit (when not allQoE targets can be met) the controller is able to make compromises,using an appropriate QoS-to-QoE gradient, to discern between slices thathave a QoE model less sensitive or more sensitive to QoS parameterchanges.

The system 1 also employs a QoS architecture that supports non-standardapplications. This QoS architecture may be beneficially used forfacilitating negotiation between OTT service providers and operators inorder to meet the requirements of 5G applications. Thus, beneficially,the QoS architecture used in this system 1 may be used to support otherQoS parameters than the six standard QoS parameters currently defined in3GPP (i.e. Resource Type, Priority level, Packet Delay Budget, PacketError Rate, Averaging Window, and Maximum Data Burst Volume).

The main features of this exemplary QoS architecture include one or moreof the following:

-   -   i) support for flexible QoS profiles based on QoS bitmap, in        which, for example:        -   each bit represents one QoS parameter;        -   the length of the QoS bitmap may be flexible (e.g. it may be            set to 32 bit, 64 bits, 128 bits, as appropriate);        -   if the QoS bitmap is 32 bits, then 16 bits may be allocated            to standard QoS parameters and 16 bits may be allocated to            non-standard QoS parameters;    -   ii) support for new applications defined by OTT service        providers:        -   non-standard or new parameters may be included (e.g. after            standard QoS parameters);        -   for each QoS type, there are two parts: a standard QoS            parameter part (defined by 3GPP); and a            non-standard/non-3GPP QoS parameter part (which may be            defined by network operators and OTT service providers);        -   some QoS parameters may be normalized QoS parameters in            order to facilitate a particular OTT/operator/vendor.

User Equipment (UE)

FIG. 2 is a block diagram illustrating the main components of the UE(mobile device 3) shown in FIG. 1. As shown, the UE includes atransceiver circuit 31 which is operable to transmit signals to and toreceive signals from the connected node(s) via one or more antenna 33.Although not necessarily shown in FIG. 2, the UE will of course have allthe usual functionality of a conventional mobile device (such as a userinterface 35) and this may be provided by any one or any combination ofhardware, software and firmware, as appropriate. A controller 37controls the operation of the UE in accordance with software stored in amemory 39. The software may be pre-installed in the memory 39 and/or maybe downloaded via the telecommunication network 1 or from a removabledata storage device (RMD), for example. The software includes, amongother things, an operating system 41 and a communications control module43. The communications control module 43 is responsible for handling(generating/sending/receiving) signaling messages and uplink/downlinkdata packets between the UE 3 and other nodes, including (R)AN nodes 5,core network nodes, and application functions.

(R)AN Node

FIG. 3 is a block diagram illustrating the main components of anexemplary (R)AN node 5 (base station) shown in FIG. 1. As shown, the(R)AN node 5 includes a transceiver circuit 51 which is operable totransmit signals to and to receive signals from connected UE(s) 3 viaone or more antenna 53 and to transmit signals to and to receive signalsfrom other network nodes (either directly or indirectly) via a networkinterface 55. The network interface 55 typically includes an appropriatebase station—base station interface (such as X2/Xn) and an appropriatebase station—core network interface (such as S1/N1/N2/N3).

A controller 57 controls the operation of the (R)AN node 5 in accordancewith software stored in a memory 59. The software may be pre-installedin the memory 59 and/or may be downloaded via the telecommunicationnetwork 1 or from a removable data storage device (RMD), for example.The software includes, among other things, an operating system 61 and acommunications control module 63. The communications control module 63is responsible for handling (generating/sending/receiving) signalingbetween the (R)AN node 5 and other nodes, such as the UE 3 and the corenetwork nodes/AFs 12. Such signaling includes appropriately formattedrequests and responses relating to network automation in slice-basednetworks (using reinforcement learning) and/or flexible QoS profiles(based on a QoS bitmap and/or the like).

Core Network Node

FIG. 4 is a block diagram illustrating the main components of a genericcore network node (or function) shown in FIGS. 5 to 11. It will beappreciated that the same block diagram may be applicable to the AF 12and the nodes of the OAM as well. As shown, the core network nodeincludes a transceiver circuit 71 which is operable to transmit signalsto and to receive signals from other nodes (including the UE 3 and the(R)AN node 5) via a network interface 75. A controller 77 controls theoperation of the core network node in accordance with software stored ina memory 79. The software may be pre-installed in the memory 79 and/ormay be downloaded via the telecommunication network 1 or from aremovable data storage device (RMD), for example. The software includes,among other things, an operating system 81 and at least a communicationscontrol module 83. The communications control module 83 is responsiblefor handling (generating/sending/receiving) signaling between the corenetwork node and other nodes, such as the UE 3, (R)AN node 5, the AFs12, and other core network nodes. Such signaling includes appropriatelyformatted requests and responses relating to network automation inslice-based networks (using reinforcement learning) and/or flexible QoSprofiles (based on a QoS bitmap and/or the like).

In order to address the aforementioned problems, the specificationoffers multiple exemplary solutions in order to deal with the QoSprovisioning for network automation under differentdeployment/implementation scenarios.

Exemplary embodiments include:

Embodiment 1: new ML-based multi-agent architecture, which can optimallyallocate network resources to meet a target user's QoE in slice-basednetworks;

Embodiment 2: New QoS architecture, which can support non-standardisedapplications

Embodiment 1: ML-Based Multi-Agent Architecture, which can EfficientlyAllocate Network Resource Against Target User QoE in Slice-Based Network

The main idea of this embodiment is to efficiently allocate networkresources across network slices such that a target user's QoE issatisfied, in an automated machine-learning based manner.

In order to efficiently allocate network resources across the multiplenetwork slices, the inventors propose a ML-based multi-agentarchitecture.

FIG. 5 illustrates schematically an example of such an ML-basedMulti-Agent Architecture.

As shown in FIG. 5, this ML-based multi-Agent Architecture has a centralcontroller and one agent per network slice. The main task of the centralcontroller is to allocate network resources across slices based ondifferent factors, such as network load, slice priority, QoS tolerance,user QoE and cost, or any other relevant parameters/attributes. Slicepriority is the priority between the different slices, and is normallydecided by the operators' policy. The term QoS tolerance used hereinrefers to a QoS-to-QoE gradient, which is a function mapping thesensitivity of the slices' or users' QoE to changes in their QoSparameters (latency, throughput, CPUs, etc.). For instance, if a slicehas an average target QoE X (e.g. MOS score) and requires latency Y (QoSparameter) to meet the target, the QoS tolerance function would indicatethe percentage of loss/gain QoE G such that QoE is G*X uponincreases/decreases in latency performance H (when latency experience bythe service is H*Y). The QoS may be a vector of network/computingparameters. In this way, the central controller can use this informationto make compromises (e.g. minimize the amount of QoE target violations)in situations of resource deficit or conflicts. In other words, thecentral controller is configured to use the QoS-to-QoE gradient toestimate how costly it is, in terms of QoE changes, to change some QoSparameter(s)/network resources.

Cost is the price needed to be paid for a slice to use the networkresource against a target QoE.

There are three main tasks for the agent in a network slice:

Learn mapping between network resources (i.e. network QoS) and user QoE.

Learn QoS-to-QoE gradient (i.e. QoS tolerance parameter). This parameteris essentially the gradient of the mapping function learnt in step 1(i.e., how QoE changes as a function of the allocated networkresources). In this way, in the presence of resource deficit, thecentral controller can decide the compromise of network resourcesallocated to each slice with minimal user QoE violation. Given theML-based nature of this problem, it is assumed this parameter is also anoutput of the ML model.

Provide the central controller current and target user QoE. The centralcontroller uses the learnt models and the QoS-to-QoE gradient (i.e. QoStolerance parameter) to allocate network resources across network slicesoptimally with minimal user QoE violation.

Reinforcement learning is preferred to be used by the agent to manageresource within a network slice, and model can be trained based on theuser QoE data.

Agent is the agent in the network slice, Environment is the wholenetwork system, Reward is QoE, State is the network performance, andAction is the agent selecting network parameters.

FIG. 6 demonstrates an exemplary procedure for network resourceallocation in multiple network slices environment.

Step 1: Based on training data, Agent learns the mapping between networkresource and user QoE by using reinforcement learning. The networkresources are end-to-end and include both physical and virtualisedresources such as radio network, transport and core network resources.Network resource parameters include for example, bandwidth, power,modulation scheme, the number of servers or any other relatedparameters/attributes. If value iteration approach is adopted, theresidual gradient algorithm is used for the mapping between networkresource and user QoE. If Q-learning approach is adopted, state/actionpair to Q-function is resource allocation for each user/resourceallocation to their QoE.

Step 2: Agent provides the Central Controller current and target userQoE. Agent also provides its QoS tolerance in the network slice, andmapping between network resource and QoE with QoS-to-QoE gradient. Ifvalue iteration approach is adopted, the residual gradient algorithm isused for the mapping between network resource and user QoE. IfQ-learning approach is adopted, state/action pair to Q-function isresource allocation for each user/resource allocation to their QoE.

Step 3: The central controller calculates the resource based on networkresource allocation state in the entire network, slice priority, QoStolerance in the network slice, current and target QoE and the cost tochange resource allocation among network slices or any other oneparameter or combination of relevant parameters/attributes defined bythe network operators or service providers.

Step 4: Central Controller allocates network resource to the requirednetwork slice.

Scenario 1: MDAS Requests Network Policy from OAMMF

In the 5G networks, both the central controller and the agent of slicecan be part of the OAM system. They can potentially be implemented inthe Orchestrator and/or slice management functions (e.g. SliceManagement Function and/or Slice Subnet Management Function). Thecentral controller and the agent of slice can also be implemented as apart of 5G network function system.

FIG. 7 demonstrates the procedures of network resource allocation inslice-based 5G system, in which an MDAS obtains related network policiesfrom an OAM MF.

The OAM MF(s) requests notifications from the MDAS on changes in theanalytics information by sending Management Service Subscription Requestmessage or any other service procedure or message for the purpose ofrequest to subscribe a management service from the MDAS. The message mayinclude parameters; at least one of user QoE analytical result andnetwork QoS/KPIs analytical results. User QoE analytical result is thesatisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS analytical results can be delay,jitter, throughput, or other kinds of relevant QoS parameters.

The MDAS subscribes to the NWDAF's service by sendingNnwdaf_Events_Subscription_Subscribe message or any other serviceprocedure or message for the purpose of subscribing analytics and/orstatistics information from the NWDAF.

The MDAS acknowledges OAM MF(s)′ Subscription Request via a ManagementService Subscription Response message.

The AF provides user data to the NWDAF, and one or more core network NFprovide(s) a part of network data to the MDAS and a part of network datato the NWDAF. The Core Network NF(s) can be SMF, AMF, PCF, or any otherNFs that can provide network data to the NWDAF. If the AF is trusted bythe network operator, then the AF can send data to the NWDAF directly;if the AF is untrusted by the network operator, the AF will be connectedto the NWDAF via a NEF. The part of network data sent to the MDAS by thecore network NF can be QoS flow-related data, such as QoS flow Bit Rate,QoS flow Packet Delay, QoS flow packet Error Rate or any other relevantparameter. The part of network data sent to the MDAS by the core networkNF can be whole network-related data, such as Registered Subscribers ofnetwork and network Slice Instance, End-to-end Latency of 5G Network,Downlink latency in gNB, Upstream Throughput for Network and NetworkSlice Instance, Downstream Throughput for Single Network Slice Instance,Upstream Throughput at N3 interface, Downstream Throughput at N3interface, Number of PDU sessions of network and network Slice Instance,Virtualized Resource Utilization of Network Slice Instance.

The NDWAF performs data analytics based on the collected user servicedata and part of network data, and the analytical results may includeparameters, at least one of user QoE analytical result or one ofcombined user QoE analytical result, network QoS/KPIs analyticalresults, and QoS tolerance. User QoE analytical result is thesatisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS tolerance is QoS-to-QoE gradient.

The NWDAF notifies the MDAS with its analytics result information bysending a Nnwdaf_Events_Subscription_Notify message or any other serviceprocedure or message for the purpose of notifying analytics and/orstatistics information from the NWDAF which may include parameters atleast one of user QoE analytical result and QoS tolerance. User QoEanalytical result is the satisfactory score of users, and it can be arange of numbers, a percentage or a range of grades. QoS tolerance isQoS-to-QoE gradient.

The MDAS requests network policies, such as SLA, slice priority andcost, from the OAM MF.

The OAM MF provides the MDAS the required network policies.

The MDAS performs data analytics based on the collected network data,which includes radio network data, transport network data, core networkdata, and the NWDAF's analytical information (including QoS tolerance),the network policies, and its analytical results may include parameters,at least one of combined user QoE analytical result and network QoS/KPIsanalytical results, the gradient mapping between network resource anduser QoE, current and target user QoE, and cost. User QoE analyticalresult is the satisfactory score of users, and it can be a range ofnumbers, a percentage or a range of grades. QoS analytical results canbe delay, jitter, throughput, or other kinds of QoS parameters.

The MDAS notifies the OAM MF(s) its analytics result information bysending Network slice instance capacity modification or any otherservice procedure or message for the purpose of notifying analyticsand/or statistics information which may include parameters at least oneof user QoE analytical result and network QoS/KPIs analytical results,the mapping between network resource and user QoE with QoS-to-QoEgradient, current and target user QoE, and cost. User QoE analyticalresult is the satisfactory score of users, and it can be a range ofnumbers, a percentage or a range of grades. QoS analytical results canbe delay, jitter, throughput, or other kinds of QoS parameters.

The OAM MF(s) analyses the notified analytical results from the MDAS asa new request to modify the capacity of the network slice instances.After analyzing the request based on network load, SLA, slice priority,QoS tolerance, user QoE and cost, if it is needed, the OAM MF(s) willidentify the related network slice(s), derive new network requirements,and initiates modification of the capacity of identified networkslice(s) (e.g. increase the number of related NFs in the identifiednetwork slice(s)). It also can modify an existing NSI by using NetworkSlice Configuration service or any other dedicated service or procedurefor the purpose. The action by the OAM MF(s) could be conducted at theconstituents, i.e., network slice subnets and/or at the constituent NFsof network slice.

Scenario 2: MDAS Requests Network Policy from PCF

FIG. 8 demonstrates the procedures of network resource allocation inslice-based 5G system, in which an MDAS obtains related network policiesfrom a PCF.

The OAM MF(s) requests notifications from the MDAS on changes in theanalytics information by sending Management Service Subscription Requestmessage or any other service procedure or message for the purpose ofrequest to subscribe a management service from the MDAS. The message mayinclude parameters; at least one of user QoE analytical result andnetwork QoS/KPIs analytical results. User QoE analytical result is thesatisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS analytical results can be delay,jitter, throughput, or other kinds of relevant QoS parameters.

The MDAS subscribes to the NWDAF's service by sending aNnwdaf_Events_Subscription_Subscribe message or any other serviceprocedure or message for the purpose of subscribing analytics and/orstatistics information from the NWDAF.

The MDAS acknowledges the OAM MF(s)′ Subscription Request via aManagement Service Subscription Response message.

The AF provides user data to the NWDAF, and one or more core network NFprovide(s) a part of network data to the MDAS and a part of network datato the NWDAF. The Core Network NF(s) can be SMF, AMF, PCF, or other NFsthat can provide network data to the NWDAF. If the AF is trusted by thenetwork operator, then the AF can send data to the NWDAF directly; ifthe AF is not trusted by the network operator, then the AF will beconnected to the NWDAF via a NEF. The part of network data sent to theMDAS by the core network NF(s) can be QoS flow-related data, such as QoSflow Bit Rate, QoS flow Packet Delay, QoS flow packet Error Rate. Thepart of network data sent to the MDAS by the core network NF(s) can bewhole network-related data, such as Registered Subscribers of networkand network Slice Instance, End-to-end Latency of 5G Network, Downlinklatency in gNB, Upstream Throughput for Network and Network SliceInstance, Downstream Throughput for Single Network Slice Instance,Upstream Throughput at N3 interface, Downstream Throughput at N3interface, Number of PDU sessions of network and network Slice Instance,Virtualised Resource Utilization of Network Slice Instance.

The NDWAF performs data analytics based on the collected user servicedata and part of network data, and the analytical results may includeparameters, at least one of user QoE analytical result or one ofcombined user QoE analytical result, network QoS/KPIs analyticalresults, and QoS tolerance. User QoE analytical result is thesatisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS tolerance is QoS-to-QoE gradient.

The NWDAF notifies the MDAS with its analytics result information bysending a Nnwdaf_Events_Subscription_Notify message or any other serviceprocedure or message for the purpose of notifying analytics and/orstatistics information from the NWDAF which may include parameters atleast one of user QoE analytical result and QoS tolerance. User QoEanalytical result is the satisfactory score of users, and it can be arange of numbers, a percentage or a range of grades.QoS tolerance isQoS-to-QoE gradient.

The MDAS requests network policies, such as SLA, slice priority andcost, from the PCF.

The PCF provides the MDAS the required network policies.

The MDAS performs data analytics based on the collected network data,which includes radio network data, transport network data, core networkdata, and the NWDAF's analytical information (including QoS tolerance),the network policies, and its analytical results may include parameters,at least one of combined user QoE analytical result and network QoS/KPIsanalytical results, the gradient mapping between network resource anduser QoE, current and target QoE and cost. User QoE analytical result isthe satisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS analytical results can be delay,jitter, throughput, or other kinds of QoS parameters.

The MDAS notifies the OAM MF(s) its analytics result information bysending Network slice instance capacity modification or any otherservice procedure or message for the purpose of notifying analyticsand/or statistics information which may include parameters at least oneof user QoE analytical result and network QoS/KPIs analytical results,the mapping between network resource and user QoE with QoS-to-QoEgradient, current and target QoE and cost. User QoE analytical result isthe satisfactory score of users, and it can be a range of numbers, apercentage or a range of grades. QoS analytical results can be delay,jitter, throughput, or other kinds of QoS parameters.

The OAM MF(s) analyses the notified analytical results from the MDAS asa new request to modify the capacity of the network slice instances.After analysing the request based on network load, SLA, slice priority,QoS tolerance, user QoE and cost, if it is needed, the OAM MF(s) willidentify the related network slice(s), derive new network requirements,and initiates modification of the capacity of identified networkslice(s) (e.g. increase the number of related NFs in the identifiednetwork slice(s)). It also can modify an existing NSI by using NetworkSlice Configuration service or any other dedicated service or procedurefor the purpose. The action by the OAM MF(s) could be conducted at theconstituents, i.e., network slice subnets and/or at the constituent NFsof network slice.

Embodiment 2: QoS Architecture, which can Support Non-StandardApplications

The main idea of this embodiment is to propose a new QoS architecturethat can facilitate the negotiation between OTT service providers andoperators and meets the requirements of 5G applications.

FIG. 9 illustrates an example of such a QoS architecture, and itshighlights are as follows:

-   -   It supports flexible QoS profiles based on QoS bitmap.    -   Each bit represented one QoS parameter

The length for the QoS bitmap is flexible. It is 32 bit, and can beextended to 64 bits or 128 bits in the future. If it is 32 bits, 16 bitswill be allocated to standard QoS parameters and 16 bits will beallocated to non-standard QoS parameters.

It supports new applications defined by OTT service providers.

It starts with standard QoS parameters, and followed by non-standard ornew parameters

For each QoS type, there are two parts: Standard QoS parameters, whichare defined by 3GPP; Non-standard QoS parameters, which are defined bynetwork operators and OTT service providers

Some QoS parameters can be normalised QoS parameters to facilitateOTT/operator/vendor

Currently, there are six standard QoS parameters defined in 3GPP, i.e.Resource Type (which may be GBR, Delay critical GBR, or Non-GBR),Priority level, Packet Delay Budget, Packet Error Rate, Averagingwindow, and Maximum Data Burst Volume.

As shown in FIG. 10, for example, an OTT service provider has a newapplication, which uses standard QoS parameters Resource Type, Prioritylevel, Packet Delay Budget, Packet Error Rate, and a non-standard QoSparameter “user response time”. Its QoS bitmap will be, in this example,“111100” for standard QoS parameters, and “00000001” for non-standardQoS parameters. The overall QoS bitmap is “11110000000001”. It is easyto accommodate new applications.

FIG. 10 illustrates schematically an exemplary QoS bitmap of a newapplication.

FIG. 11 illustrates schematically an exemplary procedure for QoSnegotiation of a new application.

After an OTT service provider and an operator signs a SLA, an AF invokesthe Nnef_QoSNegotiation_Create service by sending a NEF aNnef_QoSNegotiation_Create request message or any other serviceprocedure or message for the purpose of initiating QoS negotiation withthe network. The message may include parameters; at least one of whichmay be an appropriately formatted QoS bitmap.

Based on the AF's request, the NEF sends, to the PCF, aNpcf_QoSnegotiation_Create request message or any other serviceprocedure or message for the purpose of initiating QoS negotiation withnetwork. The message may include parameters; at least one of which maybe an appropriately formatted QoS bitmap.

The PCF may request from the UDR the stored QoS policies using an NudrDM Query (Policy Data) service operation.

The UDR provides all the stored policies to the PCF.

The PCF determines QoS parameters/policies for the new application basedon the information provided by the AF and other available information(e.g. PCC rules).

The PCF sends an acknowledge message to the NEF with the new QoSparameters/policies.

The NEF sends an acknowledge message to the AF with the new QoSparameters/policies.

SUMMARY

In summary, the central controller uses QoE targets rather than QoStargets and learns the best QoS parameters to meet such QoE targets.When there is a resource deficit (when not all QoE targets can be met)the central controller is able to make compromises, using the QoS-to-QoEgradient to discern between slices that have a QoE model less or moresensitive to QoS parameter changes.

Beneficially, the above described exemplary embodiments include,although they are not limited to, one or more of the followingfunctionalities:

A new ML-based multi-agent architecture, which can optimally allocatenetwork resource against target user QoE in multiple network slicesenvironment has been proposed. In this architecture, there is an agentto use RL algorithms to find the mapping network resource and targetuser QoE with QoS-to-QoE gradient. This Agent also learns and monitorsits QoS tolerance of user applications in the network slice. QoStolerance is a novel parameter. The central controller calculate andallocate the resource based on network resource allocation state in theentire network, slice priority, QoS tolerance, the mapping betweennetwork resource and user QoE with QoS-to-QoE gradient, current andtarget user QoE and the cost to change resource allocation among networkslices. In this way, network operators can adjust network resource basedon user QoE and therefore optimize network resource allocation.

Using the gradient mapping between network resource and user QoE basedon QoS tolerance and learned from reinforcement learning pave a new wayfor resource allocation in slice-based network.

The above description proposes a QoS architecture to facilitate thenegotiation between OTT service providers and operators and accommodatenew 5G applications. It not only can support applications with new QoSrequirement that can be standardized, but also can support newapplications defined by OTT service providers.

The above embodiments describe exemplary methods in which:

Embodiment 1

Core network NFs provides OAM user QoE and QoS tolerance information.

The gradient mapping between network resource allocation parameter anduser QoE based on QoS tolerance is utilized for network automation.

OAM allocates network resource based on network resource allocationstate in the entire network, slice priority, QoS tolerance, the mappingbetween network resource and user QoE with QoS-to-QoE gradient, currentand target user QoE and the cost to change resource allocation amongnetwork slices.

Embodiment 2

OTT service providers and operators negotiated QoS provisioning usingthe QoS architecture is based on QoS bitmap, and can supportnon-standardized new applications defined by OTT service providers.

Benefits

The proposed embodiments allow optimizing the network resourceallocation based on the data analytics utilizing QoS tolerance.

Modifications and Alternatives

Detailed embodiments have been described above. As those skilled in theart will appreciate, a number of modifications and alternatives can bemade to the above embodiments whilst still benefiting from theinventions embodied therein. By way of illustration, exemplaryalternatives and modifications will now be described.

In the above embodiments, a central controller is used. However, it willbe appreciated that a distributed controller may be used instead, or aplurality of local ‘sub-controllers’ may be used to perform thefunctionalities of a central controller in a specific part of thenetwork.

In the above description, the UE, the (R)AN node, and the core networknode are described for ease of understanding as having a number ofdiscrete modules (such as the communication control modules). Whilstthese modules may be provided in this way for certain applications, forexample where an existing system has been modified to implement theinvention, in other applications, for example in systems designed withthe inventive features in mind from the outset, these modules may bebuilt into the overall operating system or code and so these modules maynot be discernible as discrete entities. These modules may also beimplemented in software, hardware, firmware or a mix of these.

Each controller may comprise any suitable form of processing circuitryincluding (but not limited to), for example: one or more hardwareimplemented computer processors; microprocessors; central processingunits (CPUs); arithmetic logic units (ALUs); input/output (TO) circuits;internal memories/caches (program and/or data); processing registers;communication buses (e.g. control, data and/or address buses); directmemory access (DMA) functions; hardware or software implementedcounters, pointers and/or timers; and/or the like.

In the above embodiments, a number of software modules were described.As those skilled in the art will appreciate, the software modules may beprovided in compiled or un-compiled form and may be supplied to the UE,the (R)AN node, and the core network node as a signal over a computernetwork, or on a recording medium. Further, the functionality performedby part or all of this software may be performed using one or morededicated hardware circuits. However, the use of software modules ispreferred as it facilitates the updating of the UE, the (R)AN node, andthe core network node in order to update their functionalities.

The above embodiments are also applicable to ‘non-mobile’ or generallystationary user equipment.

The information relating to a QoS tolerance may comprise a function(e.g. a QoS-to-QoE gradient) mapping a sensitivity of a particular QoEto changes in at least one associated QoS parameter. The informationidentifying a mapping between network resource allocations and a QoEassociated with a user may be obtained using machine learning (e.g.value iteration, Q-learning, and/or the like). The informationidentifying a mapping between network resource allocations and a QoEassociated with a user may be obtained by an agent associated with saidat least one slice.

The allocating said at least a portion of said network resources to saidat least one slice may be further based on at least one of: a currentnetwork load; a service level agreement (SLA); a respective priorityassociated with said plurality of slices; and a cost associated withsaid network resources.

The network resources may comprise at least one of: physical networkresources; virtualised network resources; radio network resources;transport network resources; core network resources; and end-to-endnetwork resources.

For a particular service, there may be at least one QoS parameterindicated via said first field and at least one QoS parameter indicatedvia said second field.

The method for configuring a QoS may further comprise: obtaininginformation identifying at least one requirement for said service; anddetermining said QoS parameter based on said at least one requirement.

The second field may comprise a plurality of bits, each bit indicating arespective application defined by a service provider. The size of thebitmap may be flexible (e.g. 32 bits, 64 bits, or 128 bits). Thesignalling said determined at least one QoS parameter may comprisetransmitting, to a Network Exposure Function (NEF) or a Policy ControlFunction (PCF), a message for negotiating a QoS parameter (e.g. a‘Nnef_QoSNegotiation_Create’ message, a ‘Npcf_QoSnegotiation_Create’message, and/or the like).

While embodiments of the invention have been illustrated and describedin detail in the drawings and foregoing description, such illustrationand description are to be considered illustrative or exemplary and notrestrictive. It will be understood that changes and modifications may bemade by those of ordinary skill within the scope of the followingclaims. In particular, the present invention covers further embodimentswith any combination of features from different embodiments describedabove and below. Additionally, statements made herein characterizing theinvention refer to an embodiment of the invention and not necessarilyall embodiments.

The terms used in the claims should be construed to have the broadestreasonable interpretation consistent with the foregoing description. Forexample, the use of the article “a” or “the” in introducing an elementshould not be interpreted as being exclusive of a plurality of elements.Likewise, the recitation of “or” should be interpreted as beinginclusive, such that the recitation of “A or B” is not exclusive of “Aand B,” unless it is clear from the context or the foregoing descriptionthat only one of A and B is intended. Further, the recitation of “atleast one of A, B and C” should be interpreted as one or more of a groupof elements consisting of A, B and C, and should not be interpreted asrequiring at least one of each of the listed elements A, B and C,regardless of whether A, B and C are related as categories or otherwise.Moreover, the recitation of “A, B and/or C” or “at least one of A, B orC” should be interpreted as including any singular entity from thelisted elements, e.g., A, any subset from the listed elements, e.g., Aand B, or the entire list of elements A, B and C.

Abbreviations and Terminology

-   3GPP 3rd Generation Partnership Project-   5GS 5G System-   5QI 5G QoS Indicator-   AF Application Function-   KPI Key Performance Indicator-   MDAS Management Data Analytics Service-   ML Machine Learning-   NEF Network Exposure Function-   NF Network Function-   NWDAF Network Data Analytics Function-   OAM Operations, Administration and Maintenance-   OTT Over the Top-   PCC Policy and Charging Control-   PCF Policy Control Function-   QoE Quality of Experience-   QoS Quality of Service-   RL Reinforcement learning-   SMF Session Management Function-   UE User Equipment

LIST OF REFERENCES

-   [1] 3GPP TS 23.501 V15.2.0-   [2] 3GPP TS 23.502 V15.2.0-   [3] 3GPP TS 23.503 V15.2.0-   [4] 3GPP TR 23.791 V0.5.0

1. A method for facilitating allocation of network resources to at leastone slice in a communication network, the method comprising: obtaininginformation identifying a mapping between network resource allocationsand a respective quality of experience (QoE) associated with a user;obtaining information relating to a quality of service (QoS) tolerancefor a particular QoE; providing to a controller for allocating at leasta portion of the network resources to the at least one slice: i) theinformation identifying a mapping between network resource allocationsand a QoE; ii) the information relating to the QoS tolerance; and iii)information identifying a current QoE and a target QoE associated withat least one user.
 2. The method according to claim 1, wherein theinformation relating to a QoS tolerance comprises a function (e.g. aQoS-to-QoE gradient) mapping a sensitivity of a particular QoE tochanges in at least one associated QoS parameter.
 3. The methodaccording to claim 1, wherein the information identifying a mappingbetween network resource allocations and a QoE associated with a user isobtained using machine learning.
 4. The method according to claim 1,wherein the information identifying a mapping between network resourceallocations and a QoE associated with a user is obtained by an agentassociated with the at least one slice.
 5. A method for allocatingnetwork resources to at least one slice in a communication network, themethod comprising: obtaining: i) information identifying a mappingbetween network resource allocations and a quality of experience (QoE)associated with a user; ii) information relating to a quality of service(QoS) tolerance for a particular QoE; and iii) information identifying acurrent QoE and a target QoE associated with at least one user; andallocating at least a portion of the network resources to the at leastone slice based on the QoS tolerance and the current QoE and the targetQoE.
 6. The method according to claim 5, wherein the allocating the atleast a portion of the network resources to the at least one slice isfurther based on at least one of: a current network load; a servicelevel agreement; a respective priority associated with the plurality ofslices; and a cost associated with the network resources.
 7. The methodaccording to claim 5, wherein the network resources comprise at leastone of: physical network resources; virtualised network resources; radionetwork resources; transport network resources; core network resources;and end-to-end network resources.
 8. A method for configuring a Qualityof Service (QoS) to be used in a communication network, the methodcomprising: determining at least one QoS parameter associated with aservice to be provided; signaling the determined at least one QoSparameter to another node using a bitmap comprising at least a firstfield and a second field, wherein: the first field comprises a pluralityof bits, each bit indicating a respective QoS parameter selected from: aresource type QoS parameter, a priority level QoS parameter, a packetdelay budget QoS parameter, a packet error rate QoS parameter, anaveraging window QoS parameter, and a maximum data burst volume QoSparameter; and the second field is configurable to indicate a QoSparameter different to the QoS parameters indicated via the first field.9. The method according to claim 8, wherein, for a particular service,there is at least one QoS parameter indicated via the first field and atleast one QoS parameter indicated via the second field.
 10. The methodaccording to claim 8, further comprising: obtaining informationidentifying at least one requirement for the service; and determiningthe QoS parameter based on the at least one requirement.
 11. The methodaccording to claim 8, wherein the second field comprises a plurality ofbits, each bit indicating a respective application defined by a serviceprovider.
 12. The method according to claim 8, wherein a size of thebitmap is flexible.
 13. The method according to claim 8, wherein thesignaling the determined at least one QoS parameter comprisestransmitting, to a Network Exposure Function or a Policy ControlFunction (PCF), a message for negotiating a QoS parameter (e.g. a‘Nnef_QoSNegotiation_Create’ message, a ‘Npcf_QoSnegotiation_Create’message, and/or the like).
 14. A method for configuring a Quality ofService to be used in a communication network, the method comprising:receiving signaling indicating at least one QoS parameter using a bitmapcomprising at least a first field and a second field, wherein: the firstfield comprises a plurality of bits, each bit indicating a respectiveQoS parameter selected from: a resource type QoS parameter, a prioritylevel QoS parameter, a packet delay budget QoS parameter, a packet errorrate QoS parameter, an averaging window QoS parameter, and a maximumdata burst volume QoS parameter; and the second field is configurable toindicate a QoS parameter different to the QoS parameters indicated viathe first field; and determining at least one QoS parameter associatedwith a service to be provided based on the bitmap.
 15. A networkfunction for facilitating allocation of network resources to at leastone slice in a communication network, the network function comprising acontroller, and a transceiver, wherein the controller is configured toperform the method according to claim
 1. 16. A network function forallocating network resources to at least one slice in a communicationnetwork, the network function comprising a controller, and atransceiver, wherein the controller is configured to perform the methodaccording to claim
 5. 17. The network function according to claim 16,wherein the controller is further configured to allocate the at least aportion of the network resources to the at least one slice based on atleast one of: a current network load; a service level agreement (SLA); arespective priority associated with the plurality of slices; and a costassociated with the network resources.
 18. A network function forconfiguring a Quality of Service (QoS) to be used in a communicationnetwork, the network function comprising a controller, and atransceiver, wherein the controller is configured to perform a methodaccording to claim
 8. 19. A network function for configuring a Qualityof Service (QoS) to be used in a communication network, the networkfunction comprising a controller, and a transceiver, wherein thecontroller is configured to perform a method according to claim 14.